5.3 Conjugate gradient method在线视频

现代优化方法（英文）课程列表：

Chapter 1. Introduction

-1.1 About optimization

--1.1 About optimization

-1.2 Classification of optimization

--1.2 Classification of optimization

-1.3 Preliminaries in convex analysis

--1.3 Preliminaries in convex analysis

-课件

-【作业须知】

-练习题

Chapter 2. Fundamentals of Unconstrained Optimization

-2.1 What is a Solution?

--2.1 What is a Solution?

-2.2 Optimality Conditions Ⅰ

--2.2 Optimality Conditions Ⅰ

-2.3 Optimality Conditions Ⅱ

--2.3 Optimality Conditions Ⅱ

-2.4 Line search strategy

--2.4 Line search strategy

-2.5 Search direction Ⅱ

--2.5 Search direction Ⅱ

-2.6 Convergence

--2.6 Convergence

-课件

-【作业须知】

-作业

Chapter 3. Line Search Methods

-3.1 Exact Step Length

--3.1 Exact Step Length

-3.2 The Wolfe conditions

--3.2 The Wolfe conditions

-3.3 Inexact Line Search II

--3.3 Inexact Line Search II

-3.4 Convergence of Line Search Methods

--3.4 Convergence of Line Search Methods

-3.5 Convergence Rate

--3.5 Convergence Rate

-课件

-【作业须知】

-作业

Chapter 4. Trust Region Methods

-4.1 Main Idea of Trust Region Methods

--4.1 Main Idea of Trust Region Methods

-4.2 Trust-Region Algorithm

--4.2 Trust-Region Algorithm

-4.3 Solving Subproblem

--4.3 Solving Subproblem

-4.4 Solving Subproblem II

--4.4 Solving Subproblem II

-4.5 Convergence

--4.5 Convergence

-课件

-【作业须知】

-作业

Chapter 5. Conjugate Gradient Methods

-5.1 Conjugate direction method

--5.1 Conjugate direction method

-5.2 Property of conjugate direction method

--5.2 Property of conjugate direction method

-5.3 Conjugate gradient method

--5.3 Conjugate gradient method

-5.4 Rate of convergence

--5.4 Rate of convergence

-5.5 Nonlinear conjugate gradient method

--5.5 Nonlinear conjugate gradient method

-5.6 Convergence of nonlinear conjugate gradient method

--5.6 Convergence of nonlinear conjugate gradient method

-课件

-【作业须知】

-作业

Chapter 6. Semismooth Newton's Method

-6.1 Semismoothness

--6.1 Semismoothness

-6.2 Semismooth Newton's Method

-6.3 Support Vector Machine

--6.3 Support Vector Machine

-6.4 Semismooth Newtons' Method for SVM

--6.4 Semismooth Newtons' Method for SVM

-6.5 Exploring Sparsity in SVM

--6.5 Exploring Sparsity in SVM

-课件

-【作业须知】

-作业

Chapter 7. Theory of Constrained Optimization

-7.1 Local and Global Solutions

--7.1 Local and Global Solutions

-7.2 Examples One

--7.2 Examples One

-7.3 Examples Two and Three

--7.3 Examples Two and Three

-7.4 Constraint Qualifications

--7.4 Constraint Qualifications

-7.5 First-Order Optimality Conditions

--7.5 First-Order Optimality Conditions

-7.6 Second Order Necessary Condition

--7.6 Second Order Necessary Condition

-7.7 Second Order Sufficient Condition

--7.7 Second Order Sufficient Condition

-7.8 Duality

--7.8 Duality

-课件

-【作业须知】

-作业

Chapter 8. Further Discussions on Constrained Optimization

-8.1 KKT conditions

--8.1 KKT conditions

-8.2 An Example

--8.2 An Example

-8.3 Dual Problem

--8.3 Dual Problem

-课件

-【作业须知】

-测试题

Chapter 9. Penalty and Augmented Lagrangian Methods

-9.1 Quadratic Penalty Method

--9.1 Quadratic Penalty Method

-9.2 Exact Penalty Function

--9.2 Exact Penalty Function

-9.3 Augmented Lagrangian Method

--9.3 Augmented Lagrangian Method

-9.4 Quadratic Penalty Method for Hypergraph Matching

--9.4 Quadratic Penalty Method for Hypergraph Matching

-9.5 Quadratic Penalty Method for Hypergraph Matching: Ⅱ

--9.5 Quadratic Penalty Method for Hypergraph Matching: Ⅱ

-9.6 Augmented Lagrangian Method for SVM

--9.6 Augmented Lagrangian Method for SVM

-9.7 Augmented Lagrangian Method for SVM: Ⅱ

--9.7 Augmented Lagrangian Method for SVM: Ⅱ

-课件

-【作业须知】

-作业

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